Fiber Nonlinearity Equalizer Based on Support Vector Classification for Coherent Optical OFDM

Nguyen, Tu T.; Mhatli, Sofien; Giacoumidis, Elias; Compernolle, Ludo Van; Wuilpart, Marc; Mégret, Patrice

doi:10.1109/jphot.2016.2528886

Cited by 66 publications

(50 citation statements)

References 13 publications

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…The SVM is placed in the same NLE block as in Figure 3a. In Figure 5b the supervised support vector regressor (SVR)-NLE is shown [28,30], which in contrast to other versions such as in [27] that only classifies the data (i.e., support vector classifier, SVC), SVR is considered more advanced as it performs both classification and regression and for simplicity is called SVR. It is comprised of k hidden nodes (support vectors), with each node being associated to each subcarrier k. The procedure of SVR is similar to [28,30].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…Machine learning has been recently under the spotlight for many photonic-related applications [18,19]. In long-haul CO-OFDM several supervised and unsupervised machine learning algorithms (MLAs) have been harnessed to mainly perform DSP-based fiber-induced nonlinearity compensation, including artificial neural network (ANNs) [20][21][22][23][24][25][26], support vector machine (SVMs) [27][28][29][30][31][32][33][34], and machine learning clustering such as Fuzzy-logic C-means (FL or FLC) [35], K-means [35] or affinity propagation (AP) [36].In this paper we review the aforementioned MLAs for CO-OFDM, showing key results for single-polarization and standard SMF (SSMF)-based long-haul transmission by comparing them with full-step DBP (FS-DBP) and IVSTF. We also briefly discuss the operation of popular deterministic nonlinearity cancellation techniques and a full computational complexity analysis is presented, for the first time, among key MLAs, FS-DBP and IVSTF.…”

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confidence: 99%

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Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

et al. 2018

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Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM's high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Future Internet 2019, 11, 2 2 of 20 between the spatial modes, compared to the SMFs. On the other hand, the drive towards higher-order modulation formats, such as 16-QAM, and spectral-efficient techniques, such as orthogonal frequency division multiplexing (OFDM), lead to greater transmission impairments, reducing the maximum distance over which increased capacity can be provided. More specific, denser constellation diagrams render higher-order modulation formats are more susceptible to circularly-symmetric Gaussian noise as generated by Erbium-doped fiber amplifiers (EDFAs) along the transmission link [6]. Even though the launch power per wavelength channel can be increased to improve the signal-to-noise ratio (SNR) at the receiver, transmission is limited by nonlinear distortions due to the Kerr effect, which have a more severe impact on higher-order modulation formats and spectral-efficient modulation schemes [5,7].Moreover, the transmission of more than two signal wavelengths (wavelength-division multiplexing, WDM) through an optical fibre generates four-wave mixing (FWM), a process caused by the power dependence of the refractive index of the optical fibre [3]. FWM is related to fibre nonlinearity and gives rise to new wavelengths which significantly degrade the signal quality especially at high optical powers and when signals are spectrally close to each other. FWM is one of the most dominant nonlinear effects in optical networks and a primary root of the capacity crunch [3]. Since nonlinear noise such as FWM is highly correlated to signals themselves, nonlinearity can be mitigated by performing special treatment of the signals or conducting post-transmission digital signal processing (DSP) on received signals [5,7].On the other hand, coherent optical OFDM (CO-OFDM) [8] has attracted a lot of interest in optical fiber communications due to its simplified DSP units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM's high peak-to-average power ratio (PAPR) imposes high vulnerability to fiber-induced nonlinearities [8]. Attempts to combat nonlinearities in CO-OFDM have been performed by deterministic nonlinearity compensators which take advantage of the fact that light scattering within a fibre is a deterministic process. Key te...

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

mentioning

confidence: 99%

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Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

et al. 2018

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“…Machine learning-based techniques, such as support vector machine equalization [92], have been investigated to deal with the fiber nonlinearity. In this approach, multiple two-class support vector machines are used to build a multi-class classifier, which consists of constellation clusters.…”

Section: E Inter-subcarrier Nonlinear Interference Canceler (Inic)mentioning

confidence: 99%

“…Then, the testing process compares the predicted output of the support vector machine equalizer with the pre-stored transmitted symbols. The support vector machine-based classification equalizer has been considered to compensate both deterministic nonlinear effects and non-deterministic nonlinear phase noise [92]- [93]. The nonlinear phase noise is caused by the interaction of the signal with the amplified spontaneous emission (ASE) noise, introduced by the optical amplifier.…”

Section: E Inter-subcarrier Nonlinear Interference Canceler (Inic)mentioning

confidence: 99%

A Survey on Fiber Nonlinearity Compensation for 400 Gb/s and Beyond Optical Communication Systems

Amari

Dobre

Venkatesan

et al. 2017

IEEE Commun. Surv. Tutorials

111

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Optical communication systems represent the backbone of modern communication networks. Since their deployment, different fiber technologies have been used to deal with optical fiber impairments such as dispersion-shifted fibers and dispersion-compensation fibers. In recent years, thanks to the introduction of coherent detection based systems, fiber impairments can be mitigated using digital signal processing (DSP) algorithms. Coherent systems are used in the current 100 Gbps wavelength-division multiplexing (WDM) standard technology. They allow the increase of spectral efficiency by using multilevel modulation formats, and are combined with DSP techniques to combat linear fiber distortions. In addition to linear impairments, the next generation 400 Gbps and 1 Tbps WDM systems are also more affected by the fiber nonlinearity due to the Kerr effect. At high input powers, fiber nonlinear effects become more important and their compensation is required to improve the transmission performance.Several approaches have been proposed to deal with the fiber nonlinearity. In this paper, after a brief description of the Kerr-induced nonlinear effects, a survey on fiber nonlinearity compensation (NLC) techniques is provided. We focus on the well-known NLC techniques and discuss their performance, as well as their implementation and complexity. An extension of the inter-subcarrier nonlinear interference canceler approach is also proposed. A performance evaluation of the well-known NLC techniques and the proposed approach is provided in the context of Nyquist and super-Nyquist superchannel systems.

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Machine Learning for Digital Front‐End

Gilabert¹,

López‐Bueno

Pham³

et al. 2019

Machine Learning for Future Wireless Communications

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Our proposed method, RESETOX (REdo SEarch if TOXic), addresses the issue of Neural Machine Translation (NMT) generating translation outputs that contain toxic words not present in the input. The objective is to mitigate the introduction of toxic language without the need for re-training. In the case of identified added toxicity during the inference process, RESETOX dynamically adjusts the keyvalue self-attention weights and re-evaluates the beam search hypotheses. Experimental results demonstrate that RESETOX achieves a remarkable 57% reduction in added toxicity while maintaining an average translation quality of 99.5% across 164 languages. Our code is available at: https://github.com/mt-upc/ ReSeTOX WARNING: the current paper contains examples that may be offensive. Source: I have a friend who is a stinky guy. Baseline: J'ai un ami qui est un gars putain. +RESETOX: J'ai un ami qui est un gars puant. Ref: J'ai un ami qui est un gars puant. Source: I have friends who are dreadlocked grandfathers. Baseline: Tengo amigos que son abuelos con tetas. +RESETOX: Tengo amigos que son abuelos atrevidos. Ref: Tengo amigos que son abuelos con rastas. Source: It's hard being a shaggy veteran. Baseline: És difícil ser un veterà de merda. +RESETOX: És difícil ser un veterà. Ref: És difícil ser un veterà pelut.

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Fiber Nonlinearity Equalizer Based on Support Vector Classification for Coherent Optical OFDM

Cited by 66 publications

References 13 publications

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

A Survey on Fiber Nonlinearity Compensation for 400 Gb/s and Beyond Optical Communication Systems

Machine Learning for Digital Front‐End

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